CausalPy - Evaluating uncertainty for Interrupted time series

[aabugaev]

Hey, @drbenvincent!

Thanks again for your previous responses once again, they’re very helpful.

I have one more question and I thought it would be better to proceed in this thread than to start the new one.

I can see that CausalPy uses the mu to calculate post_impact and post_impact_cumulative.

post_impact and post_impact_cumulative are also used to calculate credible intervals in the examples for CausalPy (so uncertainty seems to be based on mu as well).

1. Can you please help me understand, if there’s any theoretical background to using the mu and not the y_hat here?
2. Does the difference between using mu and y_hat for credible intervals has the same interpretation as Confidence Intervals (mu) and Prediction intervals (y_hat) in frequentist context?

Appreciated!